Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations2111
Missing cells0
Missing cells (%)0.0%
Duplicate rows9
Duplicate rows (%)0.4%
Total size in memory280.5 KiB
Average record size in memory136.1 B

Variable types

Categorical5
Numeric8
Boolean4

Alerts

Dataset has 9 (0.4%) duplicate rowsDuplicates
Gender is highly overall correlated with Height and 1 other fieldsHigh correlation
Height is highly overall correlated with GenderHigh correlation
NObeyesdad is highly overall correlated with Gender and 2 other fieldsHigh correlation
Weight is highly overall correlated with NObeyesdad and 1 other fieldsHigh correlation
family_history_with_overweight is highly overall correlated with NObeyesdad and 1 other fieldsHigh correlation
CAEC is highly imbalanced (58.1%) Imbalance
SMOKE is highly imbalanced (85.4%) Imbalance
SCC is highly imbalanced (73.3%) Imbalance
MTRANS is highly imbalanced (57.1%) Imbalance
FAF has 421 (19.9%) zeros Zeros
TUE has 560 (26.5%) zeros Zeros

Reproduction

Analysis started2025-03-24 17:47:19.514841
Analysis finished2025-03-24 17:47:25.424841
Duration5.91 seconds
Software versionydata-profiling vv4.15.0
Download configurationconfig.json

Variables

Gender
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
Male
1068 
Female
1043 

Length

Max length6
Median length4
Mean length4.9881573
Min length4

Characters and Unicode

Total characters10530
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 1068
50.6%
Female 1043
49.4%

Length

2025-03-24T18:47:25.592824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-24T18:47:25.637015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male 1068
50.6%
female 1043
49.4%

Most occurring characters

ValueCountFrequency (%)
e 3154
30.0%
a 2111
20.0%
l 2111
20.0%
M 1068
 
10.1%
F 1043
 
9.9%
m 1043
 
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10530
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3154
30.0%
a 2111
20.0%
l 2111
20.0%
M 1068
 
10.1%
F 1043
 
9.9%
m 1043
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10530
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3154
30.0%
a 2111
20.0%
l 2111
20.0%
M 1068
 
10.1%
F 1043
 
9.9%
m 1043
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10530
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3154
30.0%
a 2111
20.0%
l 2111
20.0%
M 1068
 
10.1%
F 1043
 
9.9%
m 1043
 
9.9%

Age
Real number (ℝ)

Distinct40
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.315964
Minimum14
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2025-03-24T18:47:25.691219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile18
Q120
median23
Q326
95-th percentile38
Maximum61
Range47
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.3570781
Coefficient of variation (CV)0.2614364
Kurtosis2.7985818
Mean24.315964
Median Absolute Deviation (MAD)3
Skewness1.5213261
Sum51331
Variance40.412442
MonotonicityNot monotonic
2025-03-24T18:47:25.770352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
21 236
11.2%
23 218
10.3%
26 213
10.1%
18 212
10.0%
19 169
 
8.0%
22 163
 
7.7%
20 150
 
7.1%
24 95
 
4.5%
25 82
 
3.9%
17 69
 
3.3%
Other values (30) 504
23.9%
ValueCountFrequency (%)
14 1
 
< 0.1%
15 1
 
< 0.1%
16 20
 
0.9%
17 69
 
3.3%
18 212
10.0%
19 169
8.0%
20 150
7.1%
21 236
11.2%
22 163
7.7%
23 218
10.3%
ValueCountFrequency (%)
61 1
 
< 0.1%
56 1
 
< 0.1%
55 5
0.2%
52 1
 
< 0.1%
51 2
 
0.1%
48 1
 
< 0.1%
47 1
 
< 0.1%
46 2
 
0.1%
45 3
0.1%
44 6
0.3%

Height
Real number (ℝ)

High correlation 

Distinct51
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7016201
Minimum1.45
Maximum1.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2025-03-24T18:47:25.870329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.45
5-th percentile1.55
Q11.63
median1.7
Q31.77
95-th percentile1.85
Maximum1.98
Range0.53
Interquartile range (IQR)0.14

Descriptive statistics

Standard deviation0.093368402
Coefficient of variation (CV)0.054870298
Kurtosis-0.56508573
Mean1.7016201
Median Absolute Deviation (MAD)0.07
Skewness-0.0091150723
Sum3592.12
Variance0.0087176584
MonotonicityNot monotonic
2025-03-24T18:47:25.954287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.7 125
 
5.9%
1.75 122
 
5.8%
1.62 96
 
4.5%
1.76 96
 
4.5%
1.65 88
 
4.2%
1.6 77
 
3.6%
1.72 76
 
3.6%
1.63 75
 
3.6%
1.77 71
 
3.4%
1.71 68
 
3.2%
Other values (41) 1217
57.7%
ValueCountFrequency (%)
1.45 1
 
< 0.1%
1.46 1
 
< 0.1%
1.48 3
 
0.1%
1.49 3
 
0.1%
1.5 17
0.8%
1.51 11
 
0.5%
1.52 19
0.9%
1.53 27
1.3%
1.54 20
0.9%
1.55 32
1.5%
ValueCountFrequency (%)
1.98 2
 
0.1%
1.95 1
 
< 0.1%
1.94 1
 
< 0.1%
1.93 4
 
0.2%
1.92 4
 
0.2%
1.91 12
0.6%
1.9 7
 
0.3%
1.89 7
 
0.3%
1.88 10
0.5%
1.87 22
1.0%

Weight
Real number (ℝ)

High correlation 

Distinct1335
Distinct (%)63.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.586035
Minimum39
Maximum173
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2025-03-24T18:47:26.037158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile48.5
Q165.47
median83
Q3107.43
95-th percentile131.915
Maximum173
Range134
Interquartile range (IQR)41.96

Descriptive statistics

Standard deviation26.191163
Coefficient of variation (CV)0.30248715
Kurtosis-0.69988323
Mean86.586035
Median Absolute Deviation (MAD)21.74
Skewness0.25542213
Sum182783.12
Variance685.977
MonotonicityNot monotonic
2025-03-24T18:47:26.265450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 59
 
2.8%
70 43
 
2.0%
50 42
 
2.0%
75 40
 
1.9%
60 37
 
1.8%
65 26
 
1.2%
90 23
 
1.1%
42 22
 
1.0%
85 19
 
0.9%
78 19
 
0.9%
Other values (1325) 1781
84.4%
ValueCountFrequency (%)
39 1
< 0.1%
39.1 1
< 0.1%
39.37 1
< 0.1%
39.7 1
< 0.1%
39.85 1
< 0.1%
40 1
< 0.1%
40.2 1
< 0.1%
40.34 1
< 0.1%
41.22 1
< 0.1%
41.27 1
< 0.1%
ValueCountFrequency (%)
173 1
< 0.1%
165.06 1
< 0.1%
160.94 1
< 0.1%
160.64 1
< 0.1%
155.87 1
< 0.1%
155.24 1
< 0.1%
154.62 1
< 0.1%
153.96 1
< 0.1%
153.15 1
< 0.1%
152.72 1
< 0.1%

family_history_with_overweight
Boolean

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
True
1726 
False
385 
ValueCountFrequency (%)
True 1726
81.8%
False 385
 
18.2%
2025-03-24T18:47:26.322487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

FAVC
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
True
1866 
False
245 
ValueCountFrequency (%)
True 1866
88.4%
False 245
 
11.6%
2025-03-24T18:47:26.353413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

FCVC
Real number (ℝ)

Distinct180
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4189863
Minimum1
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2025-03-24T18:47:26.416538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.52
Q12
median2.39
Q33
95-th percentile3
Maximum3
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.53399595
Coefficient of variation (CV)0.22075196
Kurtosis-0.63708303
Mean2.4189863
Median Absolute Deviation (MAD)0.39
Skewness-0.43305151
Sum5106.48
Variance0.28515167
MonotonicityNot monotonic
2025-03-24T18:47:26.519403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 658
31.2%
2 610
28.9%
1 34
 
1.6%
2.97 14
 
0.7%
2.05 12
 
0.6%
2.94 12
 
0.6%
2.91 12
 
0.6%
2.74 11
 
0.5%
2.03 11
 
0.5%
2.92 11
 
0.5%
Other values (170) 726
34.4%
ValueCountFrequency (%)
1 34
1.6%
1.01 2
 
0.1%
1.03 1
 
< 0.1%
1.04 2
 
0.1%
1.05 2
 
0.1%
1.06 2
 
0.1%
1.07 1
 
< 0.1%
1.08 3
 
0.1%
1.1 1
 
< 0.1%
1.11 1
 
< 0.1%
ValueCountFrequency (%)
3 658
31.2%
2.99 4
 
0.2%
2.98 9
 
0.4%
2.97 14
 
0.7%
2.96 11
 
0.5%
2.95 9
 
0.4%
2.94 12
 
0.6%
2.93 7
 
0.3%
2.92 11
 
0.5%
2.91 12
 
0.6%

NCP
Real number (ℝ)

Distinct256
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6856514
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2025-03-24T18:47:26.608995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12.66
median3
Q33
95-th percentile3.75
Maximum4
Range3
Interquartile range (IQR)0.34

Descriptive statistics

Standard deviation0.77807883
Coefficient of variation (CV)0.28971699
Kurtosis0.38542151
Mean2.6856514
Median Absolute Deviation (MAD)0
Skewness-1.1069503
Sum5669.41
Variance0.60540667
MonotonicityNot monotonic
2025-03-24T18:47:26.686366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 1208
57.2%
1 205
 
9.7%
4 74
 
3.5%
2.99 17
 
0.8%
2.98 13
 
0.6%
2.97 9
 
0.4%
3.99 9
 
0.4%
2.81 8
 
0.4%
2.88 8
 
0.4%
1.1 7
 
0.3%
Other values (246) 553
26.2%
ValueCountFrequency (%)
1 205
9.7%
1.01 4
 
0.2%
1.02 2
 
0.1%
1.03 2
 
0.1%
1.04 1
 
< 0.1%
1.05 3
 
0.1%
1.06 2
 
0.1%
1.07 3
 
0.1%
1.08 5
 
0.2%
1.09 2
 
0.1%
ValueCountFrequency (%)
4 74
3.5%
3.99 9
 
0.4%
3.98 1
 
< 0.1%
3.97 1
 
< 0.1%
3.95 1
 
< 0.1%
3.94 1
 
< 0.1%
3.91 2
 
0.1%
3.9 2
 
0.1%
3.89 2
 
0.1%
3.88 1
 
< 0.1%

CAEC
Categorical

Imbalance 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
Sometimes
1765 
Frequently
242 
Always
 
53
no
 
51

Length

Max length10
Median length9
Mean length8.8702037
Min length2

Characters and Unicode

Total characters18725
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSometimes
2nd rowSometimes
3rd rowSometimes
4th rowSometimes
5th rowSometimes

Common Values

ValueCountFrequency (%)
Sometimes 1765
83.6%
Frequently 242
 
11.5%
Always 53
 
2.5%
no 51
 
2.4%

Length

2025-03-24T18:47:26.778248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-24T18:47:26.828784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sometimes 1765
83.6%
frequently 242
 
11.5%
always 53
 
2.5%
no 51
 
2.4%

Most occurring characters

ValueCountFrequency (%)
e 4014
21.4%
m 3530
18.9%
t 2007
10.7%
s 1818
9.7%
o 1816
9.7%
S 1765
9.4%
i 1765
9.4%
y 295
 
1.6%
l 295
 
1.6%
n 293
 
1.6%
Other values (7) 1127
 
6.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18725
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 4014
21.4%
m 3530
18.9%
t 2007
10.7%
s 1818
9.7%
o 1816
9.7%
S 1765
9.4%
i 1765
9.4%
y 295
 
1.6%
l 295
 
1.6%
n 293
 
1.6%
Other values (7) 1127
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18725
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 4014
21.4%
m 3530
18.9%
t 2007
10.7%
s 1818
9.7%
o 1816
9.7%
S 1765
9.4%
i 1765
9.4%
y 295
 
1.6%
l 295
 
1.6%
n 293
 
1.6%
Other values (7) 1127
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18725
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 4014
21.4%
m 3530
18.9%
t 2007
10.7%
s 1818
9.7%
o 1816
9.7%
S 1765
9.4%
i 1765
9.4%
y 295
 
1.6%
l 295
 
1.6%
n 293
 
1.6%
Other values (7) 1127
 
6.0%

SMOKE
Boolean

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
False
2067 
True
 
44
ValueCountFrequency (%)
False 2067
97.9%
True 44
 
2.1%
2025-03-24T18:47:26.871994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

CH2O
Real number (ℝ)

Distinct201
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0080531
Minimum1
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2025-03-24T18:47:26.930099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.585
median2
Q32.48
95-th percentile3
Maximum3
Range2
Interquartile range (IQR)0.895

Descriptive statistics

Standard deviation0.61294955
Coefficient of variation (CV)0.3052457
Kurtosis-0.87925382
Mean2.0080531
Median Absolute Deviation (MAD)0.45
Skewness-0.10502701
Sum4239
Variance0.37570716
MonotonicityNot monotonic
2025-03-24T18:47:27.020287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 467
22.1%
1 221
 
10.5%
3 163
 
7.7%
2.17 16
 
0.8%
2.04 15
 
0.7%
2.65 15
 
0.7%
2.15 14
 
0.7%
2.01 14
 
0.7%
2.36 14
 
0.7%
1.03 13
 
0.6%
Other values (191) 1159
54.9%
ValueCountFrequency (%)
1 221
10.5%
1.01 10
 
0.5%
1.02 11
 
0.5%
1.03 13
 
0.6%
1.04 4
 
0.2%
1.05 6
 
0.3%
1.06 4
 
0.2%
1.07 4
 
0.2%
1.08 7
 
0.3%
1.09 2
 
0.1%
ValueCountFrequency (%)
3 163
7.7%
2.99 7
 
0.3%
2.98 10
 
0.5%
2.97 5
 
0.2%
2.96 6
 
0.3%
2.95 6
 
0.3%
2.94 3
 
0.1%
2.93 5
 
0.2%
2.92 3
 
0.1%
2.91 3
 
0.1%

SCC
Boolean

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
False
2015 
True
 
96
ValueCountFrequency (%)
False 2015
95.5%
True 96
 
4.5%
2025-03-24T18:47:27.079125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

FAF
Real number (ℝ)

Zeros 

Distinct257
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0103126
Minimum0
Maximum3
Zeros421
Zeros (%)19.9%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2025-03-24T18:47:27.132311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.125
median1
Q31.67
95-th percentile2.68
Maximum3
Range3
Interquartile range (IQR)1.545

Descriptive statistics

Standard deviation0.85061316
Coefficient of variation (CV)0.84193062
Kurtosis-0.62067528
Mean1.0103126
Median Absolute Deviation (MAD)0.8
Skewness0.49854641
Sum2132.77
Variance0.72354275
MonotonicityNot monotonic
2025-03-24T18:47:27.212543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 421
 
19.9%
1 243
 
11.5%
2 199
 
9.4%
3 77
 
3.6%
0.99 15
 
0.7%
0.03 14
 
0.7%
0.11 13
 
0.6%
0.01 13
 
0.6%
1.98 13
 
0.6%
0.98 12
 
0.6%
Other values (247) 1091
51.7%
ValueCountFrequency (%)
0 421
19.9%
0.01 13
 
0.6%
0.02 10
 
0.5%
0.03 14
 
0.7%
0.04 7
 
0.3%
0.05 8
 
0.4%
0.06 5
 
0.2%
0.07 11
 
0.5%
0.08 2
 
0.1%
0.09 8
 
0.4%
ValueCountFrequency (%)
3 77
3.6%
2.97 1
 
< 0.1%
2.94 2
 
0.1%
2.93 1
 
< 0.1%
2.89 4
 
0.2%
2.88 3
 
0.1%
2.87 1
 
< 0.1%
2.85 1
 
< 0.1%
2.83 2
 
0.1%
2.82 1
 
< 0.1%

TUE
Real number (ℝ)

Zeros 

Distinct813
Distinct (%)38.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6578612
Minimum0
Maximum2
Zeros560
Zeros (%)26.5%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2025-03-24T18:47:27.293818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.625
Q31
95-th percentile2
Maximum2
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.60892621
Coefficient of variation (CV)0.92561502
Kurtosis-0.54863476
Mean0.6578612
Median Absolute Deviation (MAD)0.485
Skewness0.61852392
Sum1388.745
Variance0.37079113
MonotonicityNot monotonic
2025-03-24T18:47:27.378592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 560
26.5%
1 292
 
13.8%
2 109
 
5.2%
0.631 6
 
0.3%
0.002 6
 
0.3%
0.413 4
 
0.2%
0.128 4
 
0.2%
0.003 4
 
0.2%
1.366 4
 
0.2%
0.94 4
 
0.2%
Other values (803) 1118
53.0%
ValueCountFrequency (%)
0 560
26.5%
0.001 3
 
0.1%
0.002 6
 
0.3%
0.003 4
 
0.2%
0.004 1
 
< 0.1%
0.005 1
 
< 0.1%
0.008 2
 
0.1%
0.009 3
 
0.1%
0.01 1
 
< 0.1%
0.011 1
 
< 0.1%
ValueCountFrequency (%)
2 109
5.2%
1.992 1
 
< 0.1%
1.991 1
 
< 0.1%
1.984 1
 
< 0.1%
1.981 1
 
< 0.1%
1.978 1
 
< 0.1%
1.973 1
 
< 0.1%
1.971 1
 
< 0.1%
1.97 1
 
< 0.1%
1.967 1
 
< 0.1%

CALC
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
Sometimes
1401 
no
639 
Frequently
 
70
Always
 
1

Length

Max length10
Median length9
Mean length6.9128375
Min length2

Characters and Unicode

Total characters14593
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowno
2nd rowSometimes
3rd rowFrequently
4th rowFrequently
5th rowSometimes

Common Values

ValueCountFrequency (%)
Sometimes 1401
66.4%
no 639
30.3%
Frequently 70
 
3.3%
Always 1
 
< 0.1%

Length

2025-03-24T18:47:27.460346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-24T18:47:27.503947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sometimes 1401
66.4%
no 639
30.3%
frequently 70
 
3.3%
always 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 2942
20.2%
m 2802
19.2%
o 2040
14.0%
t 1471
10.1%
s 1402
9.6%
S 1401
9.6%
i 1401
9.6%
n 709
 
4.9%
l 71
 
0.5%
y 71
 
0.5%
Other values (7) 283
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14593
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2942
20.2%
m 2802
19.2%
o 2040
14.0%
t 1471
10.1%
s 1402
9.6%
S 1401
9.6%
i 1401
9.6%
n 709
 
4.9%
l 71
 
0.5%
y 71
 
0.5%
Other values (7) 283
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14593
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2942
20.2%
m 2802
19.2%
o 2040
14.0%
t 1471
10.1%
s 1402
9.6%
S 1401
9.6%
i 1401
9.6%
n 709
 
4.9%
l 71
 
0.5%
y 71
 
0.5%
Other values (7) 283
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14593
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2942
20.2%
m 2802
19.2%
o 2040
14.0%
t 1471
10.1%
s 1402
9.6%
S 1401
9.6%
i 1401
9.6%
n 709
 
4.9%
l 71
 
0.5%
y 71
 
0.5%
Other values (7) 283
 
1.9%

MTRANS
Categorical

Imbalance 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
Public_Transportation
1580 
Automobile
457 
Walking
 
56
Motorbike
 
11
Bike
 
7

Length

Max length21
Median length21
Mean length18.128375
Min length4

Characters and Unicode

Total characters38269
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPublic_Transportation
2nd rowPublic_Transportation
3rd rowPublic_Transportation
4th rowWalking
5th rowPublic_Transportation

Common Values

ValueCountFrequency (%)
Public_Transportation 1580
74.8%
Automobile 457
 
21.6%
Walking 56
 
2.7%
Motorbike 11
 
0.5%
Bike 7
 
0.3%

Length

2025-03-24T18:47:27.574052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-24T18:47:27.620447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
public_transportation 1580
74.8%
automobile 457
 
21.6%
walking 56
 
2.7%
motorbike 11
 
0.5%
bike 7
 
0.3%

Most occurring characters

ValueCountFrequency (%)
o 4096
10.7%
i 3691
 
9.6%
t 3628
 
9.5%
a 3216
 
8.4%
n 3216
 
8.4%
r 3171
 
8.3%
l 2093
 
5.5%
b 2048
 
5.4%
u 2037
 
5.3%
P 1580
 
4.1%
Other values (13) 9493
24.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 38269
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 4096
10.7%
i 3691
 
9.6%
t 3628
 
9.5%
a 3216
 
8.4%
n 3216
 
8.4%
r 3171
 
8.3%
l 2093
 
5.5%
b 2048
 
5.4%
u 2037
 
5.3%
P 1580
 
4.1%
Other values (13) 9493
24.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 38269
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 4096
10.7%
i 3691
 
9.6%
t 3628
 
9.5%
a 3216
 
8.4%
n 3216
 
8.4%
r 3171
 
8.3%
l 2093
 
5.5%
b 2048
 
5.4%
u 2037
 
5.3%
P 1580
 
4.1%
Other values (13) 9493
24.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 38269
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 4096
10.7%
i 3691
 
9.6%
t 3628
 
9.5%
a 3216
 
8.4%
n 3216
 
8.4%
r 3171
 
8.3%
l 2093
 
5.5%
b 2048
 
5.4%
u 2037
 
5.3%
P 1580
 
4.1%
Other values (13) 9493
24.8%

NObeyesdad
Categorical

High correlation 

Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
Obesity_Type_I
351 
Obesity_Type_III
324 
Obesity_Type_II
297 
Overweight_Level_I
290 
Overweight_Level_II
290 
Other values (2)
559 

Length

Max length19
Median length16
Mean length16.192326
Min length13

Characters and Unicode

Total characters34182
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal_Weight
2nd rowNormal_Weight
3rd rowNormal_Weight
4th rowOverweight_Level_I
5th rowOverweight_Level_II

Common Values

ValueCountFrequency (%)
Obesity_Type_I 351
16.6%
Obesity_Type_III 324
15.3%
Obesity_Type_II 297
14.1%
Overweight_Level_I 290
13.7%
Overweight_Level_II 290
13.7%
Normal_Weight 287
13.6%
Insufficient_Weight 272
12.9%

Length

2025-03-24T18:47:27.687067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-24T18:47:27.745852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
obesity_type_i 351
16.6%
obesity_type_iii 324
15.3%
obesity_type_ii 297
14.1%
overweight_level_i 290
13.7%
overweight_level_ii 290
13.7%
normal_weight 287
13.6%
insufficient_weight 272
12.9%

Most occurring characters

ValueCountFrequency (%)
e 5095
14.9%
_ 3663
 
10.7%
I 3059
 
8.9%
i 2655
 
7.8%
t 2383
 
7.0%
y 1944
 
5.7%
O 1552
 
4.5%
s 1244
 
3.6%
v 1160
 
3.4%
g 1139
 
3.3%
Other values (17) 10288
30.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34182
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 5095
14.9%
_ 3663
 
10.7%
I 3059
 
8.9%
i 2655
 
7.8%
t 2383
 
7.0%
y 1944
 
5.7%
O 1552
 
4.5%
s 1244
 
3.6%
v 1160
 
3.4%
g 1139
 
3.3%
Other values (17) 10288
30.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34182
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 5095
14.9%
_ 3663
 
10.7%
I 3059
 
8.9%
i 2655
 
7.8%
t 2383
 
7.0%
y 1944
 
5.7%
O 1552
 
4.5%
s 1244
 
3.6%
v 1160
 
3.4%
g 1139
 
3.3%
Other values (17) 10288
30.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34182
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 5095
14.9%
_ 3663
 
10.7%
I 3059
 
8.9%
i 2655
 
7.8%
t 2383
 
7.0%
y 1944
 
5.7%
O 1552
 
4.5%
s 1244
 
3.6%
v 1160
 
3.4%
g 1139
 
3.3%
Other values (17) 10288
30.1%

Interactions

2025-03-24T18:47:24.550351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:20.304892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:20.873529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:21.419683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:22.100889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:22.702577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:23.256491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:23.849060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:24.617460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:20.376914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:20.937776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:21.470466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:22.170835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:22.765249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:23.323648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:23.915051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:24.688916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:20.442187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:21.003977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:21.544671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:22.244921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:22.832810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:23.403538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:24.120478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:24.753678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:20.506214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:21.071680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:21.604610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:22.320432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:22.903771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:23.474266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:24.190508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:24.833900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:20.570330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:21.136699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:21.789289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:22.387222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:22.982309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:23.547019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:24.267101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:24.906495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:20.638487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:21.203778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:21.878947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:22.471893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:23.036954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:23.621305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:24.336536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:24.982412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:20.741631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:21.278003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:21.954775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:22.548212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:23.118048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:23.699151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:24.406934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:25.054548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:20.803852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:21.348950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:22.026014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:22.623227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:23.189528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:23.771049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T18:47:24.478742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-24T18:47:27.836948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeCAECCALCCH2OFAFFAVCFCVCGenderHeightMTRANSNCPNObeyesdadSCCSMOKETUEWeightfamily_history_with_overweight
Age1.0000.1540.1640.008-0.2100.1370.0580.189-0.0030.348-0.1050.2920.1320.185-0.2980.3570.235
CAEC0.1541.0000.0980.1810.1150.1930.1290.1310.1570.0950.1670.3520.1600.0460.1320.3180.349
CALC0.1640.0981.0000.1070.1120.1370.1020.0330.0990.0950.1210.2250.0550.1040.1380.2190.012
CH2O0.0080.1810.1071.0000.1570.1910.0660.2330.2230.0880.0680.2290.1300.0740.0230.2230.228
FAF-0.2100.1150.1120.1571.0000.1520.0280.2650.3260.1140.1440.2120.0950.0670.050-0.0440.158
FAVC0.1370.1930.1370.1910.1521.0000.0910.0600.2210.2010.0320.3280.1860.0400.1700.2930.205
FCVC0.0580.1290.1020.0660.0280.0911.0000.349-0.0540.1030.0820.2930.0950.000-0.0860.2100.118
Gender0.1890.1310.0330.2330.2650.0600.3491.0000.6220.1620.1620.5560.0980.0350.1300.3960.099
Height-0.0030.1570.0990.2230.3260.221-0.0540.6221.0000.0900.2070.2060.1730.1620.0790.4620.301
MTRANS0.3480.0950.0950.0880.1140.2010.1030.1620.0901.0000.0420.1790.0700.0000.1260.1400.118
NCP-0.1050.1670.1210.0680.1440.0320.0820.1620.2070.0421.0000.2440.0450.0270.0850.0050.189
NObeyesdad0.2920.3520.2250.2290.2120.3280.2930.5560.2060.1790.2441.0000.2350.1110.2160.5750.540
SCC0.1320.1600.0550.1300.0950.1860.0950.0980.1730.0700.0450.2351.0000.0330.1280.2350.181
SMOKE0.1850.0460.1040.0740.0670.0400.0000.0350.1620.0000.0270.1110.0331.0000.0580.1290.000
TUE-0.2980.1320.1380.0230.0500.170-0.0860.1300.0790.1260.0850.2160.1280.0581.000-0.0500.187
Weight0.3570.3180.2190.223-0.0440.2930.2100.3960.4620.1400.0050.5750.2350.129-0.0501.0000.557
family_history_with_overweight0.2350.3490.0120.2280.1580.2050.1180.0990.3010.1180.1890.5400.1810.0000.1870.5571.000

Missing values

2025-03-24T18:47:25.203959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-24T18:47:25.307399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

GenderAgeHeightWeightfamily_history_with_overweightFAVCFCVCNCPCAECSMOKECH2OSCCFAFTUECALCMTRANSNObeyesdad
0Female211.6264.0yesno2.03.0Sometimesno2.0no0.01.0noPublic_TransportationNormal_Weight
1Female211.5256.0yesno3.03.0Sometimesyes3.0yes3.00.0SometimesPublic_TransportationNormal_Weight
2Male231.8077.0yesno2.03.0Sometimesno2.0no2.01.0FrequentlyPublic_TransportationNormal_Weight
3Male271.8087.0nono3.03.0Sometimesno2.0no2.00.0FrequentlyWalkingOverweight_Level_I
4Male221.7889.8nono2.01.0Sometimesno2.0no0.00.0SometimesPublic_TransportationOverweight_Level_II
5Male291.6253.0noyes2.03.0Sometimesno2.0no0.00.0SometimesAutomobileNormal_Weight
6Female231.5055.0yesyes3.03.0Sometimesno2.0no1.00.0SometimesMotorbikeNormal_Weight
7Male221.6453.0nono2.03.0Sometimesno2.0no3.00.0SometimesPublic_TransportationNormal_Weight
8Male241.7864.0yesyes3.03.0Sometimesno2.0no1.01.0FrequentlyPublic_TransportationNormal_Weight
9Male221.7268.0yesyes2.03.0Sometimesno2.0no1.01.0noPublic_TransportationNormal_Weight
GenderAgeHeightWeightfamily_history_with_overweightFAVCFCVCNCPCAECSMOKECH2OSCCFAFTUECALCMTRANSNObeyesdad
2101Female261.63107.22yesyes3.03.0Sometimesno2.49no0.070.456SometimesPublic_TransportationObesity_Type_III
2102Female261.63108.11yesyes3.03.0Sometimesno2.32no0.050.413SometimesPublic_TransportationObesity_Type_III
2103Female211.72133.03yesyes3.03.0Sometimesno1.65no1.540.912SometimesPublic_TransportationObesity_Type_III
2104Female221.73133.04yesyes3.03.0Sometimesno1.61no1.510.931SometimesPublic_TransportationObesity_Type_III
2105Female211.73131.34yesyes3.03.0Sometimesno1.80no1.730.898SometimesPublic_TransportationObesity_Type_III
2106Female211.71131.41yesyes3.03.0Sometimesno1.73no1.680.906SometimesPublic_TransportationObesity_Type_III
2107Female221.75133.74yesyes3.03.0Sometimesno2.01no1.340.599SometimesPublic_TransportationObesity_Type_III
2108Female231.75133.69yesyes3.03.0Sometimesno2.05no1.410.646SometimesPublic_TransportationObesity_Type_III
2109Female241.74133.35yesyes3.03.0Sometimesno2.85no1.140.586SometimesPublic_TransportationObesity_Type_III
2110Female241.74133.47yesyes3.03.0Sometimesno2.86no1.030.714SometimesPublic_TransportationObesity_Type_III

Duplicate rows

Most frequently occurring

GenderAgeHeightWeightfamily_history_with_overweightFAVCFCVCNCPCAECSMOKECH2OSCCFAFTUECALCMTRANSNObeyesdad# duplicates
7Male211.6270.0noyes2.01.0nono3.0no1.00.0SometimesPublic_TransportationOverweight_Level_I15
3Female211.5242.0noyes3.01.0Frequentlyno1.0no0.00.0SometimesPublic_TransportationInsufficient_Weight4
0Female161.6658.0nono2.01.0Sometimesno1.0no0.01.0noWalkingNormal_Weight2
2Female211.5242.0nono3.01.0Frequentlyno1.0no0.00.0SometimesPublic_TransportationInsufficient_Weight2
1Female181.6255.0yesyes2.03.0Frequentlyno1.0no1.01.0noPublic_TransportationNormal_Weight2
4Female221.6965.0yesyes2.03.0Sometimesno2.0no1.01.0SometimesPublic_TransportationNormal_Weight2
5Female251.5755.0noyes2.01.0Sometimesno2.0no2.00.0SometimesPublic_TransportationNormal_Weight2
6Male181.7253.0yesyes2.03.0Sometimesno2.0no0.02.0SometimesPublic_TransportationInsufficient_Weight2
8Male221.7475.0yesyes3.03.0Frequentlyno1.0no1.00.0noAutomobileNormal_Weight2